MODI: Mobile Deep Inference Made Efficient by Edge Computing. - Samuel S. Ogden; Tian Guo 001 (HotEdge, 2018)

EdgeServe: efficient deep learning model caching at the edge. - Tian Guo 001; Robert J. Walls; Samuel S. Ogden (SEC, 2019)

Challenges and Opportunities of DNN Model Execution Caching. - Guin Gilman; Samuel S. Ogden; Robert J. Walls; Tian Guo 001 (DIDL@Middleware, 2019)

CloudCoaster: Transient-aware Bursty DatacenterWorkload Scheduling. - Samuel S. Ogden; Tian Guo 001 (CoRR, 2019)

ModiPick: SLA-aware Accuracy Optimization For Mobile Deep Inference. - Samuel S. Ogden; Tian Guo 001 (CoRR, 2019)

Characterizing the Deep Neural Networks Inference Performance of Mobile Applications. - Samuel S. Ogden; Tian Guo 001 (CoRR, 2019)

MDINFERENCE: Balancing Inference Accuracy and Latency for Mobile Applications. - Samuel S. Ogden; Tian Guo 001 (IC2E, 2020)

Demystifying the Placement Policies of the NVIDIA GPU Thread Block Scheduler for Concurrent Kernels. - Guin Gilman; Samuel S. Ogden; Tian Guo 001; Robert J. Walls (SIGMETRICS Perform. Evaluation Rev., 2020)

Many Models at the Edge: Scaling Deep Inference via Model-Level Caching. - Samuel S. Ogden; Guin Gilman; Robert J. Walls; Tian Guo 001 (ACSOS, 2021)

PieSlicer: Dynamically Improving Response Time for Cloud-based CNN Inference. - Samuel S. Ogden; Xiangnan Kong; Tian Guo 001 (ICPE, 2021)

Left on Read: Reply Latency for Anxiety & Depression Screening. - M. L. Tlachac; Samuel S. Ogden (UbiComp/ISWC Adjunct, 2022)

Layercake: Efficient Inference Serving with Cloud and Mobile Resources. - Samuel S. Ogden; Tian Guo 001 (CCGrid, 2023)

Student Mental Health Screening with Text Message Metadata. - M. L. Tlachac; Samuel S. Ogden (ICMLA, 2024)

Symptom Detection with Text Message Log Distributions for Holistic Depression and Anxiety Screening. - M. L. Tlachac; Michael V. Heinz; Miranda Reisch; Samuel S. Ogden (Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2024)